LSTM input longer than output

Question

I am not sure if I understand how exactly Keras version of LSTM works. Let's say I have vector of len=20 as input and I specify keras.layers.LSTM(units=10) So in this example does the network finish after processing 50% of input or it precess the rest from start (I mean from first cell)?

Answer 1

Units are never related to the input size.
Units are related only to the output size (units = output features or channels).

An LSTM layer will always process the entire data and optionally return either the "same length (all steps)" or "no length (only last step)".

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In terms of shapes

You must have an input tensor with shape (batch, len=20, input_features).

And it will output:

• For return_sequences=False: (batch, output_features=10) - no length
• For return_sequences=True: (batch, len=20, output_features=10) - same length

Output features is always equal to units.

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See a full comprehension of the LSTM layers here: Understanding Keras LSTMs